R: Train a classification model using quadratic discriminant...

sits_qda {sits} | R Documentation

## Train a classification model using quadratic discriminant analysis

### Description

This function receives a tibble with a set of attributes X for each observation Y. These attributes are the values of the time series for each band. The function performs a quadratic discriminant analysis (qda) to obtain a predictive model. This function is a front-end to the "qda" method in the "MASS" package. Please refer to the documentation in that package for more details.

### Usage

    sits_qda(data = NULL, formula = sits_formula_logref(), ...)

### Arguments

`data` | Time series with the training samples.
`formula` | Symbolic description of the model to be fit. (default: sits_formula_logref).
`...` | Other parameters to be passed to MASS::qda function.

### Value

Model fitted to input data (to be passed to `sits_classify`)

### Author(s)

Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

### Examples

    # Retrieve the set of samples for Mato Grosso region (provided by EMBRAPA)
    # Select the NDVI band
    samples_mt_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI")
    # Train a QDA model
    qda_model <- sits_train(samples_mt_ndvi, sits_qda())
    # Classify a point
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    class <- sits_classify(point_ndvi, qda_model)

[Package sits version 0.13.0 Index]